Developer's Clew Tool Finds AI Agent Gaps, Not Claude Code, Cause Token Waste

A developer created a tool called Clew to identify wasted tokens in AI agent interactions, specifically focusing on Claude Code. Through six measurement rounds using public trace data, the developer found that Claude Code itself was not the primary source of waste. Instead, the inefficiency appeared to stem from the gaps between agents, where information might be repeated or lost. Clew employs deterministic gates, comparing grouped spans by node and normalized input, and requiring identical SHA256 hashes of outputs to detect redundancy, avoiding LLMs in its detection process. AI

IMPACT Identifies potential inefficiencies in multi-agent AI systems, suggesting areas for optimization in token usage and cost.

RANK_REASON The item describes a new tool developed by an individual to analyze AI agent performance, rather than a release from a major AI lab or a significant industry event.

Read on dev.to — LLM tag →

• Bash
• bigquery_run_query
• Claude
• Claude Code
• github-create_pull_request
• PowerShell
• RedundancyBench
• Trace Commons

• product
• infra

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